Automated scenario recognition and reporting using neural networks

ABSTRACT

An incident avoidance system includes a plurality of imaging sensors and a neural computing system. The neural network computing system is configured to receive an image feed from at least one of the plurality of imaging sensors and analyze the image feed to identify a pattern of behavior exhibited by subjects within the image feed. The neural computing system is further configured to determine whether the identified pattern of behavior matches a known type of behavior and send a command to one or more remote devices. One or both of a type of the command or the one or more remove devices are selected based on a result of the determination whether the identified pattern of behavior matches a known type of behavior.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/539,232, entitled “AUTOMATED SCENARIO RECOGNITION AND REPORTING USINGNEURAL NETWORKS”, filed on Jul. 31, 2017, the entire contents of whichare hereby incorporated by reference.

BACKGROUND OF THE INVENTION

Crowded areas, such as transit systems, malls, event centers, and thelike often have security staff monitoring video systems to ensure thatsecurity and/or emergency situations are promptly detected and dealtwith. However, in many cases, the crowded areas are too crowded or solarge that it is difficult or impossible to monitor all of the necessaryvideo feeds for a particular space without using an inordinately largestaff of security personnel. Additionally, the human element involved inrecognizing particular behaviors can result in delays in recognizing andresponding to such behavior. Improvements in such security and emergencyscenario recognition are desired.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention are directed to neural networksthat can be trained to detect various scenarios that are present withinvideo feeds, allowing the networks to simultaneously monitor largenumbers of video feeds and instantaneously recognize known scenarios.This enables real-time notifications of such events to the necessarypersonnel. The neural networks described herein may be continuallytaught to recognize new patterns of behavior, as well as to receiveadditional sensor data which may be used to determine a cause of aparticular scenario. This allows the neural network to identify apreventative action to ensure that a particular scenario does not happenor will be less likely to happen in the future.

In one embodiment, an incident avoidance system is provided. The systemmay include a plurality of imaging sensors and a neural computingsystem. The neural computing system may be configured to at a firsttime, analyze a plurality of image feeds and receive information relatedto a plurality of patterns of behavior. Each of the plurality ofpatterns of behavior may be associated with at least one of theplurality of image feeds. The information may include a characterizationof at least one pattern of behavior present in each of the plurality ofimage feeds. The system may also be configured to store each of theplurality of patterns of behavior. The system may be further configureto at a second time, receive an image feed from at least one of theplurality of imaging sensors, analyze the image feed to identify apattern of behavior exhibited by subjects within the image feed, anddetermine whether the identified pattern of behavior matches one of thestored plurality of patterns of behavior. The system may also beconfigured to send a command to one or more remote devices. One or bothof a type of the command or the one or more remove devices may beselected based on a result of the determination whether the identifiedpattern of behavior matches one of the stored plurality of patterns ofbehavior.

In another embodiment, an incident avoidance system includes a pluralityof imaging sensors and a neural computing system. The system may beconfigured to receive an image feed from at least one of the pluralityof imaging sensors and analyze the image feed to identify a pattern ofbehavior exhibited by subjects within the image feed. The system mayalso be configured to determine whether the identified pattern ofbehavior matches a known pattern of behavior and send a command to oneor more remote devices. One or both of a type of the command or the oneor more remove devices may be selected based on a result of thedetermination whether the identified pattern of behavior matches a knownpattern of behavior.

In another embodiment, a method detecting and avoiding incidents isprovided. The method may include receiving, at a neural computingsystem, an image feed from at least one imaging sensor and analyzing theimage feed to identify a pattern of behavior exhibited by subjectswithin the image feed. The method may also include determining whetherthe identified pattern of behavior matches a known pattern of behaviorand sending a command to one or more remote devices. One or both of atype of the command or the one or more remove devices may be selectedbased on a result of the determination whether the identified pattern ofbehavior matches a known pattern of behavior.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of variousembodiments may be realized by reference to the following figures.

FIG. 1 depicts an incidence avoidance system according to embodiments.

FIG. 2 depicts a general process flow for the incidence avoidance systemof FIG. 1 according to embodiments.

FIG. 3 depicts a flowchart of a process for detecting and avoidingincidents according to embodiments.

FIG. 4 is a schematic of a computer system according to embodiments.

DETAILED DESCRIPTION OF THE INVENTION

The ensuing description provides embodiment(s) only, and is not intendedto limit the scope, applicability or configuration of the disclosure.Rather, the ensuing description of the embodiment(s) will provide thoseskilled in the art with an enabling description for implementing anembodiment. It is understood that various changes may be made in thefunction and arrangement of elements without departing from the spiritand scope of this disclosure.

Embodiments of the invention(s) described herein are generally relatedto automated scenario recognition and reporting using neural networks,which may be utilized in transit systems and similar applications. Thatsaid, a person of ordinary skill in the art will recognize thatalternative embodiments may be utilized in other application, and thatalternative embodiments may utilize alternative methods, functions,and/or components than the embodiments described herein. For example,similar scenario recognition and reporting using neural networks couldbe utilized in virtually any scenario in which an area is equipped withvideo camera technology, which may be linked through an internetprotocol.

The techniques described herein are directed toward teaching a machinelearning system to recognize situations involving human behaviors (orother subjects seen in image feeds such as devices, vehicles, animals,etc.) through deep learning, and feeding this into a real-time systemaccessible by operators (e.g., transit operators). The system will“learn” through neural networks by “watching” hours of video footagesand receiving manual input to tell the machine what that particularvideo footage was, which can be from real or simulated situations, orboth.

Depending on desired functionality, this system can be used foremergency, security, or even optimization procedures. By using historicfootage or simulated scenarios, the system may be used to notify thestakeholders when a particular scenario has emerged again. An examplewould be if a person is being chased, the system may be taught by theneural network reviewing footage of the scenario happening andconstantly teach it, so that if the neural network sees a similarsituation, it knows to notify the stakeholder.

According to some embodiments, simulated scenarios may be created andfed in as a data source to closed circuit television (CCTV) cameras.Scenarios recognized or scenarios not recognized can be automaticallyreported through a browser, which makes it accessible anywhere and byanyone (with the right credentials).

The systems and methods described herein utilize neural networks toanticipate and react to human behavior and scenarios, linked with othertechnologies, such as facial recognition. These systems allow for fasterreaction times to recognized scenarios and allow the neural network toact as an intermediary to human reactions and judgment, especially inemergency situations. The neural network systems described herein do notrequire constant monitoring by humans. Rather, the systems can rely onthe machine to update in real-time and to report scenarios which theyhave been taught to recognize. These systems can recognizeout-of-the-norm activities and/or “teach” the system to recognizecertain behaviors (crowd flows, etc.). Embodiments of the invention(s)couple the neural networks with other data sources that allow the neuralnetwork(s) to identify causes of particular scenarios and/orpreventative actions. Embodiments also enable the automated reporting ofrecognized behaviors to various devices where people on the move canreceive signals directly, rather than requiring a human to report theinformation. This allows information to be transferred from the neuralnetworks through an application programming interface (API) that can beaccessed by various parties (which may be geographically dispersed) inreal-time.

Embodiments of the present inventions provide one or more of thefollowing advantages. Teaching the neural network(s) is similar totraining a human to have witnessed all of the proposed scenarios.However, in contrast to the human, the neural network can be alert 24/7,simultaneously watch multiple cameras to look for similar scenarios ithas been taught to look out for, respond instantaneously, and constantlylearn new scenarios without forgetting anything. Embodiments also allowauthorities, businesses, and operators to be alerted in real-time forvarious scenarios that they are interested in looking for. The judgementcould replace the human element or may be used as a filtration process,which starts with the machine alerting a human for a scenario it hasbeen taught to associate with something. Embodiments prepare and reportfor emergency and security scenarios can be more sophisticated and workalongside current infrastructure that supports facial and objectrecognition to further enhance authorities for security and/or emergencyrelated situations. Embodiments allow for real-time alerts to beconfigured to ensure various people receive the notifications inreal-time. This is particularly useful in emergency and/or securitysituations when normally a human reports the situation manually. Theproposed systems can feed a larger set of parties to be notified.

Turning now to FIG. 1, one embodiment of an incident avoidance system100. The system 100 may include one or more neural computing systems102. Each neural computing system 102 may include one or more speciallyprogrammed processing devices (such as specially programmed centralprocessing units (CPU), graphical processing units (GPU), and the like)that operate a network of simple elements called artificial neurons,which receive inputs, change their internal state (activation) accordingto each input, and produce an output depending on the input(s) andactivation. The network forms by connecting the output of certainneurons to the input of other neurons forming a directed, weightedgraph. For example, a neural network may consist of one or more neuronsconnected into one or more layers. For most networks, a layer containsneurons that are not connected to one another in any fashion. Rather,these neurons are connected to at least one layer of hidden nodes, someof which may be connected to a layer of output nodes. While theinterconnect pattern between layers of the network (its “topology”) maybe regular, the weights associated with the various inter-neuron linksmay vary drastically. The weights as well as the functions that computethe activation can be modified by a process called learning, which isgoverned by a learning rule. The learning rule is a rule or an algorithmwhich modifies the parameters of the neural network, in order for agiven input to the network to produce a favored output. This learningprocess typically amounts to modifying the weights and thresholds of thevariables within the network.

In the present embodiments, supervised learning is typically used by theneural computing system 102 to enable its pattern recognition behavior,although it will be appreciated that in some embodiments other machinelearning techniques may be utilized. In supervised learning, examplesare fed into the neural computing system 102 and the neural computingsystem 102 attempts to model the examples with a function to generate amapping implied by the example data. For example, in the presentembodiment, a number of video or other image feeds may be provided tothe neural computing system 102, which analyzes each of the video feeds.Manual input may be provided for each image feed that informs the neuralcomputing system 102 as to what is happening in the image feed. Forexample, the image feed may show an instance of fare evasion (such asgate jumping, tailgating, or the like), an emergency situation (such assomeone having a heart attack or other injury, a person being injured bytransit system equipment or a vehicle, a female passenger going intolabor, and/or any other situation in which one or more persons may be insome sort of danger), a problem with transit system equipment (gate ordoor being stuck, a ticket kiosk malfunctioning/being operational,etc.), excessive queueing, security concerns (fights, theft, vandalism,etc.), and/or other events. In some embodiments, multiple of thesebehaviors may be present in a single image feed. As each image feed isanalyzed, the neural computing system 102 may retrieve manually inputteddata tags associated with each feed. For example, if a particular imagefeed contains an image of a user who is tailgating at a gate, the datatag may instruct the neural network that the image feed isrepresentative of such behavior. By watching numerous (tens, hundreds,thousands, etc.) image feeds of each behavior, the neural computingsystem 102 is able to detect patterns in the movement and interactionsof people, devices, animals, and/or other subjects that are present inthe image feeds for each pattern of behavior. In this manner, the neuralcomputing system 102 may then be supplied with a real-time imagefeed(s), detect patterns of movement and other behavior within the imagefeed(s), and identify a particular pattern of behavior based on thesimilarities between the live image feed(s) and previously analyzedimage feeds tagged with the particular pattern of behavior.Additionally, data associated with the new image feed, once identified,may be stored as an additional example of the particular pattern ofbehavior that the neural computing system 102 may use as learningmaterial that may be used to help increase the accuracy of behaviordetection of the neural computing system 102.

The image feeds (both those used in the learning process and the liveimage feeds) may be captured using one or more image sensors 104. Imagesensors 104 may be cameras, such as CCTV cameras, infrared (IR) sensors,LIDAR sensors, and/or any other sensors that are able to track positionsand movements of subjects within an image feed. In some embodiments, acombination of different types of image sensors 104 may be used.

The image feeds utilized in the learning process may be historicalfeeds, simulation feeds, and/or combinations thereof. For example, thehistorical feeds may be previously captured image feeds taken from imagesensors at the same location and/or a similar location. A human analystmay review the historical image feeds and provide each image feed with atag identifying a pattern of behavior represented by the particularimage feed. The historical feeds may thus represent actual priorinstances of the tagged behavior. In contrast, the simulation feeds maybe staged scenarios that are exemplary of particular patterns ofbehavior. For example, an operator of the neural computing system 102may have people, animals, and/or other objects act out a particularscenario for capture by the image sensors 104. Just as with thehistorical feeds, a human analyst may tag the simulation feed with apattern of behavior represented by the particular feed.

In some embodiments, the data tag for any of the image feeds utilized inthe learning process may include or be associated with additional data.For example, the additional information may include instructions toperform a particular action based on detecting a particular pattern ofbehavior. The instructions may include a command for a particulartransit system device(s) and/or vehicle(s) 106 (trains, buses, cars,autonomous vehicles, etc.) to perform an action, such as open a gate 108or door, change a message on signage, activate and/or deactivate one ormore devices and/or vehicles. The instructions may also (oralternatively) include instructions to send a message to one or moreuser devices 110, devices of a transit authority 112 and/or other entityoperating the monitored area, emergency devices 114, and/or other remotedevices.

For example, when a pattern of behavior is detected that is related to asecurity concern, an issue with fare evasion, a problem with the transitsystem equipment, and/or other scenario, the instructions may force theneural computing system 102 to issue a notification to the transitauthority 112 or other entity. This may involve sending the notificationto a fare inspector device, a central security office computer, and/orother device of the transit authority 112 or other entity. Thenotification may include a description of the detected pattern ofbehavior, and optionally may include a time and location of the event,identification information (and possibly photographs) of any partiesinvolved (where available), the image feed or a portion thereof (whichcould be a single still image frame), and/or other information that maybe useful for the transit authority 112.

As another example, if fare evasion behavior is detected, theinstructions may force the neural computing system 102 to send anotification to the user that has committed the form of fare evasion.The notification may inform the user that fare evasion behavior wasdetected and allows the user to correct this behavior, for instance,where the fare evasion behavior was accidental. For example, a user ofthe system may mistakenly think they have validated their fare media andinstead may tailgate or piggyback off another user. If the user'sidentity is known (such as by using facial detection, other biometricidentification, determining the identify by a transit stem beaconcommunicating with a transit application on the user device 110 andreceiving an identifier of the user device 110 and/or the user, and/orother identification techniques) the neural computing system 102 maythen send a notification to the user's personal mobile device 110informing them that if they do not return and validate their fare media,transit authority 110 will be notified. The neural computing system 102may be trained to determine whether the user continues on or whether theuser returns to a validation device to validate the fare media. Based onthis determination, the neural computing system may alert an authorityor let the user continue on uninterrupted if they did, in fact, returnto validate the fare media.

As another example, the neural computing system 102 may detect anemergency pattern of behavior. In such cases the instructions may causethe neural computing system 102 to send a notification of the emergencydevice(s) 114. This may involve sending the notification to a portabledevice carried by emergency personnel (law enforcement, securitypersonnel, paramedics or other first responders, firefighting personnel,etc.), a computer operated by an emergency agency, and/or other deviceof an emergency agency. The notification may include a description ofthe detected pattern of behavior, and optionally may include a time andlocation of the event, ident cation information (and possiblyphotographs) of any parties involved (where available), the image feedor a portion thereof (which could be a single still image frame), and/orother information that may be useful for the emergency personnel.

In some embodiments, the neural computing system 102 may be incommunication with one or more signs 124 and/or other public messagingsystems. These may be used, for example, the inform customers of changesto routes, arrival/designation times, and/or other information. Forexample, if the neural computing system 102 determines that additionalvehicles should be put into service, upon putting the vehicles intoservice the neural computing system 102 may change signage at thetransit station to alert the users of the service change. In someembodiments, the neural computing system 102 may control a publicaddress system and cause an automated message to be played that alertsthe users of any changes. Signage 124 and other alerting systems may bemodified to have other effects on users, such as where the neuralcomputing system 102 has identified particular signage (or otherinformation) as causing a particular pattern of behavior. Theinformation may be altered in a manner that is likely tochange/eliminate the detected behavior.

The neural computing system 102 may also be configured to collectinformation from a number of external data sources and sensors. Thisinformation may be analyzed by the neural computing system 102 and maybe used to detect similarities in sets of circumstances (data from theexternal sources and/or sensors), which may be used to determine causesof the patterns of behavior and/or predict patterns of behavior prior totheir occurrence. In some embodiments, after determining a cause of aparticular pattern of behavior, the neural computing system 102 maydetermine (or be programmed with) a preventative action that may beperformed that will prevent or reduce the likelihood of a particularpattern of behavior occurring.

One example of an external data source is a non-transit event system116. For example, a concert venue, sports arena, convention center,and/or other event center may provide details to the neural computingsystem 102 about events they are hosting. For example, information abouta number of people expected to attend, who actually attended aparticular event, a location of the event (which may include a distancefrom one or more transit stations/stops), a start time/date of eachevent, an expected end time of each event, an actual end time of aparticular event, a type of event, and/or other information that relatesto a particular event. The neural computing system 102 may analyze thisdata in relation to a number of known patterns of behavior to identifyany consistencies between them. For example, an analysis of the knownpatterns of behavior in conjunction with event data for events thatoccur within a predetermined threshold (1 minute, 5 minutes, 30 minutes,1 hour, etc.) of each instance of a known pattern of behavior may revealthat for large events within 5 miles of a transit station, the transitstation may be overcrowded for a certain period of time (such as 30minutes or an hour) before and/or after the event. Thus, the neuralcomputing system 102 may make the determination that for large, nearbyevents, transit ridership is increased and may associate theovercrowding in certain instances to be caused by the event. Byidentifying a cause, the neural computing system 102 may identify apossible preventative action. In the present case of overcrowding, theneural computing system 102 may instruct and/or otherwise cause thetransit system to use larger capacity vehicles and/or scheduleadditional vehicles when a set of circumstances matching thoseidentified as a possible cause of the overcrowding are detected. Forexample, the neural computing system 102 may compare upcoming (orcurrently occurring/recently ended) events to past events of similartypes, sizes, start/end times, and the like. Upon finding similar pastevents, the neural computing system 102 may determine that the similarpast events caused overcrowding. The neural computing system 102 maythen instruct the transit system (via notifications and/or commands thatinstruct autonomous vehicles) to prepare to accommodate the largernumber of passengers that are expected. Such efforts may eliminateand/or reduce any overcrowding experienced at a particular transitstation.

In yet another example, the neural computing system 102 may receiveadditional data from the imaging sensors 104. For example, the imagingsensors 104 may count (or provide video feeds that allow the neuralcomputing system 102 to count) users at a transit station, in queue fora particular vehicle, in queue at a validation and/or access device(such as gate 108, in queue at a particular ticket kiosk

and/or other counts. This information may be used by the neuralcomputing system 102 to not only identify certain patterns of behavior(such as overcrowding and/or excessive queueing), but may also be usedas additional information for determining the cause of certain patternsof behavior. For example, when transit systems and/or devices areovercrowded and/or have too long of lines users may be tempted to try toskip transit access devices and/or tailgate due to the users being in ahurry and/or thinking that the presence of so many people may disguisethe behavior. Corrective actions may involve those that reduce excessivequeuing or overcrowding, such as sending commands or instructions to uselarger capacity vehicles 106, more vehicles 106, and/or activating moreaccess devices 108 and/or ticketing kiosks 118.

Additional sources of information that may be connected to the neuralcomputing system 102 include computer systems of the transit vehicles106 themselves. For example, the vehicles 106 may provide informationrelated to their current destinations, origins, routes, estimatedarrival times, actual arrival times, maximum available capacity, actualcapacity, and/or other information. For example, the actual capacity maybe determined based on validation information and/or video surveillanceon the vehicle 106 and/or at a boarding/disembarking point(s) of thevehicle 106. The estimated arrival times may be based on transittimetables, beacon data, and/or GPS or other location data of eachvehicle 106. This information may be useful to a neural computing system102, as the system 102 may notice that when one or more vehicles 106 arerunning behind schedule, overcrowding in the station may occur.

The neural computing system 102 may also receive data from one or moreaccess devices 106 (such as gates, turnstiles, and/or other validationdevices). Such data may include information related to whether each userhas successfully validated an access credential, such as a fare media.The data may also include information related to how many users havevalidated at each validation device (this may include real-timevalidation results). The neural computing system 102 may use suchinformation to identify fare evasion behavior when a user who has notvalidated an access credential passes beyond the validation devices.Additionally, if the neural computing system 102 detects multiple userspassing a validation device while only one validation result isreceived, the neural computing device 102 may determine that the seconduser is tailgating the first user. Additionally, rates of validationsmay be monitored to determine how busy the transit station is at aparticular time. This information may be compared to the status of eachvalidation device (whether it is activated or operational). If theneural computing system 102 has determined that a pattern behavior in animage feed matches excess queuing at access devices 106, the neuralcomputing system 102 may check to see there are any validation devicesthat are in a nonoperational (out of service or otherwisenon-functioning) state. If so, the neural computing system 102 maydetermine (perhaps based on instructions associated with itscorresponding data tag) to send a command to at least one of thenonoperational validation devices that causes the validation devices toactivate in an attempt to alleviate the excessive queuing. The numberand location of the activated validation devices 106 may be based on arate of validation and/or continued monitoring of the validation rateand queuing levels. In some embodiments, if the neural computing system102 determines that the validation rate is low, the neural computingsystem 102 may send a command to one or more operational validationdevices 106 that deactivate the validation devices 106. Suchfunctionality helps increase the lifespan of the validation devices 106,as only those validation devices 106 that are necessary to maintain adesired queuing level and/or validation rate, thereby decreasing thein-service time of validation devices 106 that are not needed at aparticular time.

In some embodiments, the neural computing system 102 may receiveinformation from one or more fare media kiosks/vending machines 118.This information may include how many of the devices 118 are currentlyin use, how many fare media have been issued/topped-up/otherwiseactivated within a certain timeframe, a usage rate of each device 118,etc. If the neural computing system 102 identifies a pattern of behaviormatching excessive queuing at such devices 118, the neural computingsystem 102 may determine whether there are any nonoperational kiosks 118that may be activated. If so, the neural computing system 102 may send acommand that causes one or more nonoperational kiosks 118 to beactivated into an in-service condition. Similarly, if the usage rate ofthe machines is sufficiently low, the neural computing system 102 mayissue a command to one or more of the kiosks 118 that causes thekiosk(s) to deactivate, thereby increasing the service life of thekiosk(s) 118.

The neural computing system 102 may also be configured to receiveinformation from a timetable information source 120. For example, theneural computing system 102 may receive transit timetables for each ofthe transit vehicles within a transit system. This information may beanalyzed upon detecting a known pattern of behavior to determine whetherthe transit schedule has an effect on any of the patterns of behavior.For example, if the neural computing system 102 notices that upon acertain point in the timetable (or for a particular scheduledroute/vehicle) there is an overcrowding issue, the neural computingsystem 102 may determine that additional vehicles need to be added,larger capacity vehicles need to be used, the timetables need to beadjusted to space out departure and/or arrival times of popularvehicles, and/or other action that may be identified from the timetabledata and be determined to have an effect on the crowds on the transitstation. The neural computing system 102 may then perform the requiredaction.

In some embodiments, the neural computing system 102 may receive datafrom a biometric data source 122. This information may include identityinformation of identified users, demographic information of users(especially in the case of facial recognition), information about thedemeanor of users (which may be used by the neural computing system 102to detect and/or predict future behavior patterns), validation results(in embodiments where biometric authentication is used as an accesscredential), and/or other information. This information may be used bythe neural computing system 102 to identify the causes of certainpatterns of behavior. For example, the neural computing system 102 maydetect that a particular user has been previously involved in one ormore out-of-the-norm patterns of behavior one or more times. Dependingon the identified pattern of behavior, the neural computing system 102may identify a response to prevent this user from repeating his earlierbehavior. For example, if the user has practiced fare evasion in thepast, the neural computing system 102 may alert the nearest fareinspector or other transit personnel to carefully watch the particularuser as he navigates the validation area of the transit station. In someembodiments, the neural computing system 102 may utilize the informationfrom the biometric data source 122 to populate information in thevarious notifications it sends. For example, user devices 110 may beidentified based on known user identities and/or validation results atbiometric validation devices. In embodiments where a notification isbeing sent to a transit authority, emergency personnel, etc. Facialrecognition systems may capture an image of the user's face, which maybe retrieved and appended to the notifications.

Other sources that may impact the operation of a transit system may beutilized. For example, a clock may be used to correlate patterns ofbehavior with different times of day. For example, the neural computingsystem 102 may detect that certain patterns of behavior occur only at acertain time or are more likely to occur at a certain time. The neuralcomputing system 102 may alert the necessary authorities of thisinformation and/or identify a preventative action that may be performedand put it into action to reduce the likelihood of the identifiedpattern of behavior. Similarly, weather data may be received andcorrelated with certain patterns of behavior.

It will be appreciated that numerous other sources of information may beconnected to the neural computing system 102, which may use such data toidentify patterns of behavior, identify causes of patterns of behavior,identify preventative actions, generate notifications, and/or performother actions related to the patterns of behavior. Moreover, whilediscussed herein individually, it will be appreciated that anycombination of one or more types of data from any combination of thesources described herein may be analyzed together or alone to detect aset of circumstances that may give rise to the pattern of behavior,identify causes of patterns of behavior, identify preventative actions,generate notifications, and/or perform another actions related to thepatterns of behavior. The neural computing system 102 may determine anycorrelations/causes, etc. based on a single instance ofdetection/correlation and/or patterns found in multiple instances over ahistory of detection.

In some instances, the neural computing system 102 may detect anout-of-the-norm pattern of behavior, but not be able to classify it as aknown pattern of behavior. This may be due to the identified behaviorbeing just different enough that an error rate of the identificationstep is too high to make a positive conclusion and/or because thedetected pattern of behavior is new and has not been previously detectedor tagged. In such embodiments, the neural computing system 102 may sendthe image feed (or portion thereof) that contains the new/unidentifiedpattern of behavior to a human analyst. The analyst may review the imagefeed and determine what type of behavior is depicted. The analyst maythen tag the image and/or instruct the neural computing system 102 toperform a particular action (such as send commands to one or more remotedevices) based on detecting such behavior in the future.

In some embodiments, a human analyst may review image feeds for some orall of the patterns of behavior that are identified by the neuralcomputing system 102. The human analyst may determine whether the neuralcomputing system 102 correctly identified each pattern of behaviorand/or whether the proper action(s) (notifications, causeidentification, preventative action, etc.) were performed. If not, theanalyst may tag the incorrectly handled image feed with the proper tagto further teach the neural computing system 102, thereby allowing theneural computing system 102 to constantly improve its error rate.

It will be appreciated that the examples of detected patterns ofbehavior, external data sources, determinations of causes, preventativeactions, notifications, and/or other information described above onlyrepresent a small subset of the possible outcomes of the neuralcomputing system 102. Any combination of the above features may be usedin conjunction with one another as is useful for a particularapplication.

FIG. 2 depicts a general process flow for the neural computing system102. For example, the neural computing system 102 may receive imagefeeds that include captured scenarios of patterns of behavior fromactual footage 202 and from simulated scenarios 204. These image feedsinclude some sort of user input from a human analyst that includes ascenario categorization, such as through data tagging. The neuralcomputing system 102 analyzing a large number of these image feeds andtheir associated data tags to learn what a particular pattern ofbehavior looks like. Once trained, the neural computing system 102 maybe fed with one or more image feeds to monitor from a real-time imagesource 206 (such as cameras, IR sensors, LIDAR, etc.), and in someembodiments will receive 3-dimensional information 208 related to anenvironment being monitored by the real-time image source 206. Thisallows the neural computing system 102 to get an idea of the environmentit is monitoring for out-of-the-norm scenarios/patterns of behavior.

The neural computing system 102 may analyze the video feeds and identifywhether they depict any known patterns of behavior based on whether theyclosely match behaviors shown in the actual footage 202 and/or thesimulated scenarios 204 (or earlier recorded images from the real-timeimage source 206 that have been analyzed and tagged by a human analyst).If a known pattern of behavior is detected, the neural computing system102 data, including notifications and/or commands to various remotedevices 210 (mobile devices, portable fare devices, emergency personneldevices, vehicles, access/validation devices, kiosks, etc.) may bepushed to an API 218 that allows the various remote devices 210 to haveaccess (either from information/commands being pushed to them or fromthe devices requesting the information via the API 218) to theinformation associated with a known pattern of behavior. If an unknownpattern of behavior is detected, the neural computing system 102 maysend a notification to a human analyst of such a result, which mayinclude the video feed containing the unknown behavior. The humananalyst may then review the feed and tag the image feed with informationassociated with a pattern of behavior exhibited within the feed. thisinformation is sent back to the neural computing system 102, which usesit to learn new patterns of behavior for future detection.

In some embodiments, the neural computing system 102 may be incommunication (over one or more wired and/or wireless networks, whichmay be public and/or private and may be secured, such as by providing anencrypted communication channel between the various systems) with one ormore external data source modules 212. The external data sourcemodule(s) 212 provide the neural computing system 102 with additionalinformation, such as sensor-based data 214 (from signage, imagingsensors, vehicle, validation devices, kiosks, weather sensors, IRsensors, biometric sensors, and the like, such as those described inrelation to FIG. 1) and/or scheduled and/or anticipated data 216 (suchas event data, transit timetables, actual arrival/departure times,purchased fare information, disruption data, and the like, such as thatdescribed in relation to FIG. 1). The data from the external data sourcemodules 212 may be used to help verify/detect patterns of behavior fromthe real-time image source 206 as well as to provide context for suchbehavior. For example, the neural computing system 102 may analyzes thisexternal data in conjunction with the detected patterns of behavior toidentify possible causes of certain behavior based on sets ofcircumstances found in the external data that are common in similarbehavior types. Once a cause is known, certain actions may be taken,including notifying authorities when the set of circumstances isoccurring (or may be about to occur, such as when all but one or twocommon features associated with a particular scenario are currentlydetected) to alert the authorities to take preventative action. In someembodiments, the neural computing system 102 may itself takepreventative action, such as by commanding a remote device to functionin a certain way (turn on/off, for example).

FIG. 3 is a flowchart of a process 300 for detecting and avoidingincidents. Process 300 may be performed by a neural network, such asneural computing system 102. In some embodiments, the neural networkmust be trained to detect out-of-the-norm patterns of behavior. This mayinclude analyzing a number of image feeds that may depict known patternsof behavior. These patterns of behavior may all be out-of-the-normbehaviors and/or may include some normal behavior (allowing the neuralnetwork to later detect out-of-the-norm behaviors that it was notspecifically trained to detect by determining that the behaviors do notmatch any of the known out-of-the-norm patterns of behavior or any knownnormal patterns of behavior). The neural network may receive informationrelated to a plurality of patterns of behavior. The information may besupplied by a human analyst who has tagged each image feed with apattern of behavior. In some embodiments, the tag may include or beassociated with a characterization of at least one known pattern ofbehavior present in each of the plurality of image feeds. Theinformation may also include an instruction on what the neural networkshould do upon detecting such behavior in the future (sendingnotifications/commands to remote devices, etc.). this information may bestored by the neural network. Once the neural network has been taught,the process 300 begins by receiving an image feed from at least oneimaging sensor at block 302. The imaging sensor may be any of the imagesensors described in relation to FIG. 1, including cameras, LIDARsensors, IR sensors, and the like. At block 304, the image feed isanalyzed to identify a pattern of behavior exhibited by subjects withinthe image feed. The neural network may determine whether the identifiedpattern of behavior matches a known pattern of behavior at block 306.This may be done by comparing the image feed (and/or data extracted fromthe image feed) to data associated with the known patterns of behavior(or in some embodiments based on additional information, such as sensordata or scheduled/derived data as described above).

For example, if no match is found, the neural network may communicatethe image feed to a human analyst device for manual review. The analystmay then review and tag the feed with information that includes aninstruction that informs the neural network of the type of behavior inthe video feed and instructs the neural network on what action to takeupon future detections. This may include what content should be includedin subsequent commands associated with the identified pattern ofbehavior, such as notifications to authorities, commands to manipulatedevices, and the like. The instruction may include a list of one or morespecific devices to notify/control upon the subsequent detection of thispattern of behavior, allowing the neural network to adapt to learn todetect and handle new patterns of behavior at all times. Upon subsequentdetection of the new patterns of behavior, the neural network mayperform the tasks provided by the human analyst.

If a match to the initially detected behavior is found, the neuralnetwork may perform an action associated with the known pattern ofbehavior (which may be do nothing if the detected pattern of behavior isnormal behavior). For example, at block 308, a command may be sent toone or more remote devices. The command may be a notification thatcauses an audio and/or visual alert to be produced by a particulardevice. In other embodiments, the command may be an electronicinstruction that causes a remote device to perform a physical function(such as turn on/off, changing wording on signage, initiating anautomated PA announcement, activate additional vehicles, etc.). Thecommand may be directed to one or more particular devices. The type ofcommand and recipient remote device(s) are based on whether theidentified pattern of behavior matches a known pattern of behaviorand/or the type of pattern of behavior that is detected. For example, ifan emergency situation is detected, the command may include anotification that details the emergency situation (time, place, partiesinvolved, type of emergency, etc.) to an emergency authority and/ortransit authority. If excessive queueing is detecting, the command mayinclude instructions to activate an additional validation device. Theseare merely examples, and numerous combinations of command content andremote device combinations may be used in a particular application basedon the detected pattern of behavior. Various commands may include, butare not limited to activating additional transit vehicles to handleoverflow, deactivating unnecessary vehicles, alerting passengers ofnew/canceled vehicles, disruptions, and/or other changes via signingchanges, automated PA announcements, direct notifications to personaluser devices, etc. Commands may also include general notifications,instructions to activate/deactivate transit equipment,open/close/lock/unlock gates or other barriers, and/or other commandsthat cause various remote devices to perform functions.

In some embodiments, process 300 involves determining a cause of aparticular pattern of behavior. For example, several instances of aparticular patterns of behavior from additional image feeds may beanalyze din conjunction with sensor data and/or scheduled/derived datareceived from one or more sensors of a transit system or other externalsources. This external data may include data that is related to atimeframe within a predetermined threshold before and/or during aparticular pattern of behavior. Based on the analysis, the neuralnetwork may identify a common set of circumstances between at least oneof the plurality of patterns of behavior and at least some of the sensordata and/or other external data. Based on this, a subsequent occurrenceof the at least one of the plurality of patterns of behavior may bepredicted by identifying the occurrence of all or a significant portionof the set of circumstances.

In some embodiments, once a cause is known and an occurrence can bepredicted, a preventative action may be identified. For example, theneural network may determine that after baseball games, a particulartransit stop near the stadium experiences excessive queueing atvalidation devices and/or fare dispensing kiosks. Based on this data,upon detecting the end of a baseball game (either based on a real endtime or an expected end time) the neural network may activate additionalkiosk and/or validation devices at or near the end of the game inanticipation of the increased volume of users to help reduce the queuelengths.

A computer system as illustrated in FIG. 4 may be incorporated as partof the previously described computerized devices. For example, computersystem 400 can represent some of the components of the neural computingsystem 102, and the like described herein. FIG. 4 provides a schematicillustration of one embodiment of a computer system 400 that can performthe methods provided by various other embodiments, as described herein.FIG. 4 is meant only to provide a generalized illustration of variouscomponents, any or all of which may be utilized as appropriate. FIG. 4,therefore, broadly illustrates how individual system elements may beimplemented in a relatively separated or relatively more integratedmanner.

The computer system 400 is shown comprising hardware elements that canbe electrically coupled via a bus 405 (or may otherwise be incommunication, as appropriate). The hardware elements may include aprocessing unit 410, including without limitation one or moreprocessors, such as one or more special-purpose processors (such asdigital signal processing chips, graphics acceleration processors,and/or the like); one or more input devices 415, which can includewithout limitation a keyboard, a touchscreen, receiver, a motion sensor,a camera, a smartcard reader, a contactless media reader, and/or thelike; and one or more output devices 420, which can include withoutlimitation a display device, a speaker, a printer, a writing module,and/or the like.

The computer system 400 may further include (and/or be in communicationwith) one or more non-transitory storage devices 425, which cancomprise, without limitation, local and/or network accessible storage,and/or can include, without limitation, a disk drive, a drive array, anoptical storage device, a solid-state storage device such as a randomaccess memory (“RAM”) and/or a read-only memory (“ROM”), which can beprogrammable, flash-updateable and/or the like. Such storage devices maybe configured to implement any appropriate data stores, includingwithout limitation, various file systems, database structures, and/orthe like.

The computer system 400 might also include a communication interface430, which can include without limitation a modem, a network card(wireless or wired), an infrared communication device, a wirelesscommunication device and/or chipset (such as a Bluetooth™ device, an502.11 device, a Wi-Fi device, a WiMAX device, an NFC device, cellularcommunication facilities, etc.), and/or similar communicationinterfaces. The communication interface 430 may permit data to beexchanged with a network (such as the network described below, to nameone example), other computer systems, and/or any other devices describedherein. In many embodiments, the computer system 400 will furthercomprise a non-transitory working memory 435, which can include a RAM orROM device, as described above.

The computer system 400 also can comprise software elements, shown asbeing currently located within the working memory 435, including anoperating system 440, device drivers, executable libraries, and/or othercode, such as one or more application programs 445, which may comprisecomputer programs provided by various embodiments, and/or may bedesigned to implement methods, and/or configure systems, provided byother embodiments, as described herein. Merely by way of example, one ormore procedures described with respect to the method(s) discussed abovemight be implemented as code and/or instructions executable by acomputer (and/or a processor within a computer); in an aspect, then,such special/specific purpose code and/or instructions can be used toconfigure and/or adapt a computing device to a special purpose computerthat is configured to perform one or more operations in accordance withthe described methods.

A set of these instructions and/or code might be stored on acomputer-readable storage medium, such as the storage device(s) 425described above. In some cases, the storage medium might be incorporatedwithin a computer system, such as computer system 400. In otherembodiments, the storage medium might be separate from a computer system(e.g., a removable medium, such as a compact disc), and/or provided inan installation package, such that the storage medium can be used toprogram, configure and/or adapt a special purpose computer with theinstructions/code stored thereon. These instructions might take the formof executable code, which is executable by the computer system 400and/or might take the form of source and/or installable code, which,upon compilation and/or installation on the computer system 400 (e.g.,using any of a variety of available compilers, installation programs,compression/decompression utilities, etc.) then takes the form ofexecutable code.

Substantial variations may be made in accordance with specificrequirements. For example, customized hardware might also be used,and/or particular elements might be implemented in hardware, software(including portable software, such as applets, etc.), or both. Moreover,hardware and/or software components that provide certain functionalitycan comprise a dedicated system (having specialized components) or maybe part of a more generic system. For example, a risk management engineconfigured to provide some or all of the features described hereinrelating to the risk profiling and/or distribution can comprise hardwareand/or software that is specialized (e.g., an application-specificintegrated circuit (ASIC), a software method, etc.) or generic (e.g.,processing unit 410, applications 445, etc.) Further, connection toother computing devices such as network input/output devices may beemployed.

Some embodiments may employ a computer system (such as the computersystem 400) to perform methods in accordance with the disclosure. Forexample, some or all of the procedures of the described methods may beperformed by the computer system 400 in response to processing unit 410executing one or more sequences of one or more instructions (which mightbe incorporated into the operating system 440 and/or other code, such asan application program 445) contained in the working memory 435. Suchinstructions may be read into the working memory 435 from anothercomputer-readable medium, such as one or more of the storage device(s)425. Merely by way of example, execution of the sequences ofinstructions contained in the working memory 435 might cause theprocessing unit 410 to perform one or more procedures of the methodsdescribed herein.

The terms “machine-readable medium” and “computer-readable medium,” asused herein, refer to any medium that participates in providing datathat causes a machine to operate in a specific fashion. In an embodimentimplemented using the computer system 400, various computer-readablemedia might be involved in providing instructions/code to processingunit 410 for execution and/or might be used to store and/or carry suchinstructions/code (e.g., as signals). In many implementations, acomputer-readable medium is a physical and/or tangible storage medium.Such a medium may take many forms, including but not limited to,non-volatile media, volatile media, and transmission media. Non-volatilemedia include, for example, optical and/or magnetic disks, such as thestorage device(s) 425. Volatile media include, without limitation,dynamic memory, such as the working memory 435. Transmission mediainclude, without limitation, coaxial cables, copper wire, and fiberoptics, including the wires that comprise the bus 405, as well as thevarious components of the communication interface 430 (and/or the mediaby which the communication interface 430 provides communication withother devices). Hence, transmission media can also take the form ofwaves (including without limitation radio, acoustic and/or light waves,such as those generated during radio-wave and infrared datacommunications).

Common forms of physical and/or tangible computer-readable mediainclude, for example, a magnetic medium, optical medium, or any otherphysical medium with patterns of holes, a RAM, a PROM, EPROM, aFLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread instructions and/or code.

The communication interface 430 (and/or components thereof) generallywill receive the signals, and the bus 405 then might carry the signals(and/or the data, instructions, etc. carried by the signals) to theworking memory 435, from which the processor(s) 405 retrieves andexecutes the instructions. The instructions received by the workingmemory 435 may optionally be stored on a non-transitory storage device425 either before or after execution by the processing unit 410.

The methods, systems, and devices discussed above are examples. Someembodiments were described as processes depicted as flow diagrams orblock diagrams. Although each may describe the operations as asequential process, many of the operations can be performed in parallelor concurrently. In addition, the order of the operations may berearranged. A process may have additional steps not included in thefigure. Furthermore, embodiments of the methods may be implemented byhardware, software, firmware, middleware, microcode, hardwaredescription languages, or any combination thereof. When implemented insoftware, firmware, middleware, or microcode, the program code or codesegments to perform the associated tasks may be stored in acomputer-readable medium such as a storage medium. Processors mayperform the associated tasks.

It should be noted that the systems and devices discussed above areintended merely to be examples. It must be stressed that variousembodiments may omit, substitute, or add various procedures orcomponents as appropriate. Also, features described with respect tocertain embodiments may be combined in various other embodiments.Different aspects and elements of the embodiments may be combined in asimilar manner. Also, it should be emphasized that technology evolvesand, thus, many of the elements are examples and should not beinterpreted to limit the scope of the invention.

Specific details are given in the description to provide a thoroughunderstanding of the embodiments. However, it will be understood by oneof ordinary skill in the art that the embodiments may be practicedwithout these specific details. For example, well-known structures andtechniques have been shown without unnecessary detail in order to avoidobscuring the embodiments. This description provides example embodimentsonly, and is not intended to limit the scope, applicability, orconfiguration of the invention. Rather, the preceding description of theembodiments will provide those skilled in the art with an enablingdescription for implementing embodiments of the invention. Variouschanges may be made in the function and arrangement of elements withoutdeparting from the spirit and scope of the invention.

The methods, systems, devices, graphs, and tables discussed above areexamples. Various configurations may omit, substitute, or add variousprocedures or components as appropriate. For instance, in alternativeconfigurations, the methods may be performed in an order different fromthat described, and/or various stages may be added, omitted, and/orcombined. Also, features described with respect to certainconfigurations may be combined in various other configurations.Different aspects and elements of the configurations may be combined ina similar manner. Also, technology evolves and, thus, many of theelements are examples and do not limit the scope of the disclosure orclaims. Additionally, the techniques discussed herein may providediffering results with different types of context awareness classifiers.

While illustrative and presently preferred embodiments of the disclosedsystems, methods, and machine-readable media have been described indetail herein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly or conventionally understood. As usedherein, the articles “a” and “an” refer to one or to more than one(i.e., to at least one) of the grammatical object of the article. By wayof example, “an element” means one element or more than one element.“About” and/or “approximately” as used herein when referring to ameasurable value such as an amount, a temporal duration, and the like,encompasses variations of ±20% or ±10%, ±5%, or +0.1% from the specifiedvalue, as such variations are appropriate to in the context of thesystems, devices, circuits, methods, and other implementations describedherein. “Substantially” as used herein when referring to a measurablevalue such as an amount, a temporal duration, a physical attribute (suchas frequency), and the like, also encompasses variations of ±20% or±10%, ±5%, or +0.1% from the specified value, as such variations areappropriate to in the context of the systems, devices, circuits,methods, and other implementations described herein. As used herein,including in the claims, “and” as used in a list of items prefaced by“at least one of’ or “one or more of’ indicates that any combination ofthe listed items may be used. For example, a list of “at least one of A,B, and C” includes any of the combinations A or B or C or AB or AC or BCand/or ABC (i.e., A and B and C). Furthermore, to the extent more thanone occurrence or use of the items A, B, or C is possible, multiple usesof A, B, and/or C may form part of the contemplated combinations. Forexample, a list of “at least one of A, B, and C” may also include AA,AAB, AAA, BB, etc.

Having described several embodiments, it will be recognized by those ofskill in the art that various modifications, alternative constructions,and equivalents may be used without departing from the spirit of theinvention. For example, the above elements may merely be a component ofa larger system, wherein other rules may take precedence over orotherwise modify the application of the invention. Also, a number ofsteps may be undertaken before, during, or after the above elements areconsidered. Accordingly, the above description should not be taken aslimiting the scope of the invention.

Also, the words “comprise”, “comprising”, “contains”, “containing”,“include”, “including”, and “includes”, when used in this specificationand in the following claims, are intended to specify the presence ofstated features, integers, components, or steps, but they do notpreclude the presence or addition of one or more other features,integers, components, steps, acts, or groups.

What is claimed is:
 1. An incident avoidance system, comprising: aplurality of imaging sensors; and a neural computing system configuredto: detect several instances of a particular pattern of behavior over ahistory of detection of the neural computing system; identify aparticular set of circumstances that has been commonly associated withthe several instances of the particular pattern of behavior over thehistory of detection of the neural computing system; identify a cause ofthe particular pattern of behavior based at least on the particular setof circumstances; determine a preventative action based on theparticular set of circumstances and the identified cause; associate thepreventative action with the particular pattern of behavior, wherein theparticular pattern of behavior is classified as a known pattern ofbehavior; receive, after determining the preventative action, an imagefeed from at least one of the plurality of imaging sensors; analyze theimage feed to identify a pattern of behavior exhibited by subjectswithin the image feed; determine that the identified pattern of behaviormatches the known pattern of behavior; and perform the preventativeaction when the particular set of circumstances is detected a subsequenttime, wherein performing the preventative action comprises sending acommand to one or more remote devices, wherein one or both of a type ofthe command or the one or more remote devices are selected based on aresult of the determination that the identified pattern of behaviormatches the known pattern of behavior, and wherein at least one of theone or more remote devices comprises a transit system device.
 2. Theincident avoidance system of claim 1, wherein: identifying the causecomprises receiving data from one or more external sources; the receiveddata is associated with a time that is within a threshold range of theidentified pattern of behavior; and the cause is further identifiedbased at least in part on at least a portion of the received data. 3.The incident avoidance system of claim 2, wherein: the received datacomprises one or more elements selected from the following: a time ofday, weather data, timetable data, non-transit event data, or transitsystem device data.
 4. The incident avoidance system of claim 1,wherein: one or both of a type of the command or the one or more remotedevices are selected further based on the identified cause.
 5. Theincident avoidance system of claim 1, wherein the neural computingsystem is further configured to: at a later time, identify a set ofcircumstances matching the identified cause; and send a command thatimplements the preventative action.
 6. A method of detecting andavoiding incidents, comprising: detecting several instances of aparticular pattern of behavior over a history of detection of a neuralcomputing system; identifying a particular set of circumstances that hasbeen commonly associated with the several instances of the particularpattern of behavior over the history of detection of the neuralcomputing system; identifying a cause of the particular pattern ofbehavior based at least on the particular set of circumstances;determining a preventative action based on the particular set ofcircumstances and the identified cause; associating the preventativeaction with the particular pattern of behavior, wherein the particularpattern of behavior is classified as a known pattern of behavior;receiving after determining the preventative action, at a neuralcomputing system, an image feed from at least one imaging sensor;analyzing the image feed to identify a pattern of behavior exhibited bysubjects within the image feed; determining that the identified patternof behavior matches the known pattern of behavior; and performing thepreventative action when the particular set of circumstances is detecteda subsequent time, wherein performing the preventative action comprisessending a command to one or more remote devices, wherein one or both ofa type of the command or the one or more remote devices are selectedbased on a result of the determination that the identified pattern ofbehavior matches the known pattern of behavior, and wherein at least oneof the one or more remote devices comprises a transit system device. 7.The method of detecting and avoiding incidents of claim 6, furthercomprising: identifying an additional plurality of patterns of behaviorfrom additional image feeds; receiving sensor data from one or moresensors of a transit system; analyzing the additional plurality ofpatterns of behavior and the sensor data to identify a common set ofcircumstances between at least one of the plurality of patterns ofbehavior and at least a portion of the sensor data; and predicting asubsequent occurrence of the at least one of the plurality of patternsof behavior.
 8. The method of detecting and avoiding incidents of claim6, further comprising: determining that the identified pattern ofbehavior involves an emergency scenario, wherein the command comprisesan alert detailing a type of the emergency scenario, and wherein the oneor more remote devices comprises an emergency agency system.
 9. Themethod of detecting and avoiding incidents of claim 6, furthercomprising: prior to receiving the image feed, analyzing a plurality ofimage feeds; receiving information related to a plurality of patterns ofbehavior, wherein each of the plurality of patterns of behavior isassociated with at least one of the plurality of image feeds, whereinthe information comprises a characterization of at least one knownpattern of behavior present in each of the plurality of image feeds; andstoring each of the plurality of known patterns of behavior.
 10. Themethod of detecting and avoiding incidents of claim 6, wherein: the atleast one imaging sensor comprises a LIDAR device.